Training Flow
This guide is for the trainers who deliver DataMiner Empower. It describes the full training flow and gives one guide per session with the flow, what is given, and the do's and don'ts.
| Section | For |
|---|---|
| Trainer Guide (this section) | How to run each session |
| Participant Enablement | What participants need and what we provide |
| Backend Packages | The Catalog packages participants deploy for the hands-on sessions |
| Content Creation | Everything that must be produced before the training |
The Flow
flowchart LR
K[1. Keynote<br/>Vision + Vanguard] --> D[2. DevOps Agents<br/>demo]
D --> AB[3. App Builder<br/>hands-on]
AB --> SA[4. Specialized Agents<br/>hands-on]
SA --> A2A[5. Agent-to-Agent<br/>demo]
| # | Session | Format | Participants create | Key takeaway | Duration |
|---|---|---|---|---|---|
| 1 | Keynote: Vision + Vanguard | Theoretical | – | Operations are heading towards AI-native, value-stream-driven work | to be defined |
| 2 | DataMiner DevOps Agents | Demonstration | – | Intent becomes implementation – the value is accelerated solution creation | to be defined |
| 3 | App Builder Workshop | Hands-on | An application | Solve an operational problem, not an infrastructure problem | to be defined |
| 4 | Specialized Agents Workshop | Hands-on | An operational agent | The most valuable agents understand how your organization operates | to be defined |
| 5 | Agent-to-Agent Collaboration | Demonstration | – | Realistic collaboration with controlled actions and human approval | to be defined |
The sessions build on each other: participants keep the same vertical from the App Builder Workshop into the Specialized Agents Workshop, and the Agent-to-Agent demo uses the same kind of operational context.
Core Philosophy
The training does not teach App Builder, DevOps Agents, DOM, ticketing, or AI agents as individual features. It creates a mindset shift.
| Participants should stop thinking... | ...and start thinking |
|---|---|
| "What application should I build?" | "What operational outcome do I want to achieve?" |
Every session reinforces the same cycle:
flowchart LR
U[Understand] --> A[Act] --> C[Create]
C -.->|Measure value & re-prioritize| U
| Step | Meaning | Question to ask participants |
|---|---|---|
| Understand | The operational problem, constraints, and desired future state | What hurts today, and what does "good" look like? |
| Act | Prioritize the highest-value opportunities | Which opportunity returns the most value right now? |
| Create | Only what is needed to prove value and achieve measurable outcomes | What is the smallest thing that proves it works? |
Key Messages to Reinforce in Every Session
1. Start with the outcome
Begin every session with these questions – never with technology:
- What problem are we solving?
- What does success look like?
- What would the future state look like?
- What business value would be created?
- How do we reduce operational effort, cost, or risk?
- How do we accelerate time-to-value?
2. Work backwards from the future state
- Define an ambitious future state
- Work backwards
- Identify the smallest valuable step
- Deliver something useful quickly
- Measure value
- Re-prioritize continuously
3. Multiple value streams
Participants may start one value stream and discover another with a much higher return. That is expected.
| Value Stream | Next Increment | Value |
|---|---|---|
| A – Incident Resolution | Current next increment | Save 5 minutes per ticket |
| B – Operational Briefing | Potential first increment | Save 45 minutes per operator per day |
→ Value Stream B should be prioritized.
We don't continue because we started. We continue because it is still the highest-value investment.
4. Operational data is the competitive advantage
The value is not AI. The value is operational telemetry, historical data, operational processes, company standards, policies, procedures, and institutional knowledge.
AI amplifies operational knowledge. Without a foundation of trusted operational data and knowledge, AI has limited value.
General Do's and Don'ts
| Do | Don't |
|---|---|
| Open every session with the outcome, not the tool | Start with a feature tour |
| Use operational language (decisions, incidents, customers, risk) | Use product jargon as the main message |
| Ask "Which decision does this help?" | Reward complexity or visual polish |
| Celebrate small, working, valuable results | Aim for completeness |
| Connect each session to the previous one | Treat sessions as separate product trainings |
| Keep AI grounded in data and policies | Present AI as magic or fully autonomous |
How Each Session Guide Is Structured
| Part | Content |
|---|---|
| At a glance | Format, what participants create, key message |
| Objective | What the session must achieve |
| What is given | Materials, environment, and data available in the session |
| Before the session | Trainer preparation |
| Session flow | Step-by-step: what the trainer does and what participants do |
| Do's and don'ts | Session-specific guidance |
| Success criteria | How you know the session worked |
| Transition | How to hand over to the next session |
Final Training Message
Participants should not leave saying "I learned App Builder" or "I learned how to create an agent."
They should leave saying:
I learned how to identify operational value, prioritize the most impactful opportunity, and rapidly create a working solution that improves operations using DataMiner's operational data, workflows, and organizational knowledge.